Methods and systems for detection of objects in a vicinity of a vehicle

Abstract

A computer implemented method for detection of objects in a vicinity of a vehicle comprises the following steps carried out by computer hardware components: acquiring radar data from a radar sensor; determining a plurality of features based on the radar data; providing the plurality of features to a single detection head; and determining a plurality of properties of an object based on an output of the single detection head.

What is claimed is: 
     
       1. A method, comprising:
 detecting, using computer hardware components of a vehicle, objects in a vicinity of a vehicle by:
 acquiring radar data from a radar sensor; 
 determining a plurality of features based on the radar data; 
 providing the plurality of features to a single detection head that comprises a plurality of sequentially arranged layers, the single detection head being free from layers arranged in parallel; 
 determining a plurality of properties of each object based on an output of the single detection head; 
 carrying out, with an ego-motion compensation module, a nearest neighbor interpolation to determine a new position of each object in a current time step and avoiding drift due to an accumulation of positional errors in positions of the objects over time by recording a residual part indicating a positional error of a movement of each object from the current time step; 
 determining the new position of each object based on a transformation grid from the current time step and a previous residual part for each object from a previous time step; 
 determining a subsequent position of each object in a subsequent time step based on the recorded residual part for each object; and 
 detecting the objects based on a regression subnet comprising the ego-motion compensation module; and 
 
 controlling, by an autonomous driving system of the vehicle, autonomous driving of the vehicle based on the plurality of properties of each object. 
 
     
     
       2. The method of  claim 1 ,
 wherein each of the plurality of features is connected to an input of the single detection head. 
 
     
     
       3. The method of  claim 1 ,
 wherein the plurality of features are determined using an artificial neural network. 
 
     
     
       4. The method of  claim 1 ,
 wherein the single detection head is trained for the plurality of properties simultaneously. 
 
     
     
       5. The method of  claim 1 ,
 wherein the plurality of properties comprises at least two of a class of the object, a size of the object, or a yaw angle of the object. 
 
     
     
       6. The method of  claim 1 , wherein detecting the objects in the vicinity of the vehicle further comprises:
 determining a radar data cube based on the radar data; 
 providing the radar data cube to a plurality of layers of a neural network; 
 resampling the output of the plurality of layers into a vehicle coordinate system; and 
 determining the plurality of features based on the resampled output. 
 
     
     
       7. The method of  claim 1 , wherein detecting the objects in the vicinity of the vehicle further comprises:
 fusing data from a plurality of radar sensors including the radar sensor; and 
 determining the plurality of features further based on the fused data. 
 
     
     
       8. The method of  claim 1 , wherein detecting the objects in the vicinity of the vehicle further comprises:
 acquiring camera data from a camera; 
 wherein the plurality of features are determined further based on the camera data. 
 
     
     
       9. The method of  claim 1 , wherein detecting the objects in the vicinity of the vehicle further comprises:
 acquiring lidar data from a lidar sensor; and 
 determining the plurality of features further based on the lidar data. 
 
     
     
       10. The method of  claim 1 , wherein detecting the objects in the vicinity of the vehicle further comprises determining an angle of arrival based on the radar data. 
     
     
       11. The method of  claim 10 , wherein the angle of arrival is determined using an artificial neural network with a plurality of layers. 
     
     
       12. The method of  claim 11 , wherein the artificial neural network further comprises a dropout layer. 
     
     
       13. The method of  claim 1 , wherein the regression subnet further comprises at least one of a u-shaped network, or a LSTM. 
     
     
       14. The method of  claim 1 , wherein the ego-motion compensation module is configured to carry out ego-motion compensation of an output of a recurrent network of the previous time step, and input the result of the ego-motion compensation into a recurrent network of the current time step. 
     
     
       15. A system comprising a plurality of computer hardware components configured to:
 detect objects in a vicinity of a vehicle by:
 acquiring radar data from a radar sensor; 
 determining a plurality of features based on the radar data; 
 providing the plurality of features to a single detection head that comprises a plurality of sequentially arranged layers, the single detection head being free from layers arranged in parallel; 
 determining a plurality of properties of each object based on an output of the single detection head; 
 carrying out, with an ego-motion compensation module, a nearest neighbor interpolation to determine a new position of each object in a current time step and avoiding drift due to an accumulation of positional errors in positions of the objects over time by recording a residual part indicating a positional error of a movement of each object from the current time step; 
 determining the new position of each object based on a transformation grid from the current time step and a previous residual part for each object from a previous time step; 
 determining a subsequent position of each object in a subsequent time step based on the recorded residual part for each object; and 
 detecting the objects based on a regression subnet comprising the ego-motion compensation module; and 
 
 control autonomous driving of the vehicle based on the plurality of properties of each object. 
 
     
     
       16. A vehicle comprising the system of  claim 15 . 
     
     
       17. A non-transitory computer readable medium comprising instructions that, when executed, configure a plurality of computer hardware components to:
 detect objects in a vicinity of a vehicle by:
 acquiring radar data from a radar sensor; 
 determining a plurality of features based on the radar data; 
 providing the plurality of features to a single detection head that comprises a plurality of sequentially arranged layers, the single detection head being free from layers arranged in parallel; 
 determining a plurality of properties of each object based on an output of the single detection head; 
 carrying out, with an ego-motion compensation module, a nearest neighbor interpolation to determine a new position of each object in a current time step and avoiding drift due to an accumulation of positional errors in positions of the objects over time by recording a residual part indicating a positional error of a movement of each object from the current time step; 
 determining the new position of each object based on a transformation grid from the current time step and a previous residual part for each object from a previous time step; 
 determining a subsequent position of each object in a subsequent time step based on the recorded residual part for each object; and 
 detecting the objects based on a regression subnet comprising the ego-motion compensation module; and 
 
 control autonomous driving of the vehicle based on the plurality of properties of each object. 
 
     
     
       18. The system of  claim 15 , wherein each of the features is connected to an input of the single detection head. 
     
     
       19. The system of  claim 15 , wherein the single detection head is trained for the plurality of properties simultaneously.